Mendelian Randomization Analysis: Smoking Status and Alzheimer's Disease
By Dr. Shea Andrews, generated on 2018-09-06
## [1] FALSEData sources
***[Smoking Status*** (Canela-Xandri et al BioRxiv 2018)]: Summary statisitcs for Smoking Status were genereated from the UK Biobank with participatns asked weather they were never, former or current somkers. Results were reported in GeneATLAS, a large database of associations between hundreds of traits and millions of variants using the UK Biobank cohort.
Late Onset Alzheimer’s disease Lambert et al 2013: The International Genomics of Alzheimer’s Project (IGAP) is a meta-analysis of 4 previously published GWAS datasets: the European Alzheimer’s Disease Imitative (EADI), the Alzheimer Disease Genetics Consortium (ADGC), Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), and Genetic and Environmental Risk in AD (GERAD) and includes a sample of 17,008 LOAD cases and 37,154 cognitively normal elder controls. Participants in IGAP were of European ancestry, the average age was 71 and 58.4% of participants were women.
Alzheimer’s Age of Onset Surivial Huang et al 2017: A GWAS of age of onset in LOAD was conducted in 14,406 AD case samples and 25,849 control samples from the IGAP using Cox proportional hazard regressions. Participants were of European ancestry, in cases the the average AAO was 74.8 and 61.7% were women, in controls the average AAE was 79.0 and 59.6% were women.
CSF Ab42, tau & ptau Deming et al 2017: A GWAS of CSF AB42, ptau and tau levels (pg/mL) was conducted in 3,146 participants. Participants were of Eurpean ancestry.
Hippocampal Volume Hibar et al 2017: A GWAS of hippocampal volume perfomed in 26,814 (ENIGMA and CHARGE consortiums) individules of European Ancestry discovered 9 independent loci.
Instrumental Variables
LD Clumping: For standard two sample MR it is important to ensure that the instruments for the exposure are independent. LD clumping can be performed using the data_clump function from the TwoSampleMR package, which uses EUR samples from the 1000 genomes project to estimate LD between SNPs and amonst SNPs that have and LD above a given threshold, only the SNP with the lowest p-value will be retained.
Proxy SNPs: SNPs associated with Smoking Status were extracted from the GWAS of LOAD, AAOS, AB42, ptau and tau. Where SNPs were not available in the outcome GWAS, the EUR thousand genomes was queried to identified potential proxy SNPs that are in linkage disequilibrium (r2 > 0.8) of the missing SNP.
Smoking Status
Smoking Status: 9411 SNPs (Table 1) were assoicated with were associated with Smoking Status at p < 5e-8. After LD clumping, 9189 of 9411 SNPs were removed.
LOAD
Of the the 222 SNPs associated with Smoking Status, 206 were available in the LOAD GWAS (Table 2).
Table 2: SNPS associated with Smoking Status avalible in LOAD GWASAAOS
Of the the 222 SNPs associated with Smoking Status, 221 were available in the AAOS GWAS (Table 3).
Table 3: SNPS associated withAB42
Of the the 222 SNPs associated with Smoking Status, 175 were available in the CSF AB42 GWAS (Table 4).
Table 4: SNPS associated with Smoking Status avalible in ab42 GWASPtau
Of the the 222 SNPs associated with Smoking Status, 191 were available in the CSF Ptau GWAS (Table 5).
Table 5: SNPS associated with Smoking Status avalible in Ptau GWASTau
Of the the 222 SNPs associated with Smoking Status, 175 were available in the CSF Tau GWAS (Table 6).
Table 6: SNPS associated with Smoking Status avalible in tau GWASHippocampal volume
Of the the 222 SNPs associated with Smoking Status, 215 were available in the Hippocampal volume (Table 7).
Table 7: SNPS associated with Smoking Status avalible in Hippocampal volume GWASData harmonization
Harmonize the exposure and outcome datasets so that the effect of a SNP on the exposure and the effect of that SNP on the outcome correspond to the same allele. The harmonise_data function from the TwoSampleMR package can be used to perform the harmonization step, by default it try’s to infer the forward strand alleles using allele frequency information. EAF were not availbe in the IGAP summary statisitics, as such the allele frequencies reported in the AAOS anaylsis were used.
Smoking Status ~ LOAD
Table 8: Harmonized Smoking Status and LOAD datasetsSmoking Status ~ AAOS
Table 9: Harmonized Smoking Status and AAOS datasetsSmoking Status ~ AB42
Table 10: Harmonized Smoking Status and AB42 datasetsSmoking Status ~ Ptau
Table 11: Harmonized Smoking Status and Ptau datasetsSmoking Status ~ Tau
Table 12: Harmonized Smoking Status and Tau datasetsSmoking Status ~ Hippocampal Volume
Table 13: Harmonized Smoking Status and Hippocampal Volume datasets
Pleiotropy
Pleiotropy was assesed using Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO), that allows for the evlation of evaluation of horizontal pleiotropy in a standared MR model. MR-PRESSO performs a global test for detection of horizontal pleiotropy and correction of pleiotropy via outlier removal.
Smoking Status ~ LOAD: The MR-PRESSO global test for pleiotropy was significant (p = 0.0105). The following SNPs were removed due to pleiotropy: No significant outliers
Smoking Status ~ AAOS: The MR-PRESSO global test for pleiotropy was non-significant.
Smoking Status ~ AB42: The MR-PRESSO global test for pleiotropy was non-significant.
Smoking Status ~ Ptau: The MR-PRESSO global test for pleiotropy was non-significant.
Smoking Status ~ Tau: The MR-PRESSO global test for pleiotropy was non-significant..
Smoking Status ~ hippocampal volume: The MR-PRESSO global test for pleiotropy was significant (p = 0.0289). The following SNPs were removed due to pleiotropy: rs11693702
Mendelian Randomization Analysis
To obtain an overall estimate of causal effect, the SNP-exposure (Major Depressive Disorder) and SNP-outcome coefficients (Alzheimer’s disease and Alzheimer’s Age of Onset) were combined in 1) a random-effects meta-analysis using an inverse-variance weighted approach (IVW); 2) a Weighted Median approach; 3) and Egger Regression. IVW is equivalent to a weighted regression of the SNP-outcome coefficients on the SNP-exposure coefficients with the intercept constrained to zero. This method assumes that all variants are valid instrumental variables based on the Mendelian randomization assumptions. The causal estimate of the IVW analysis expresses the causal increase in the outcome (or log odds of the outcome for a binary outcome) per unit change in the exposure. Weighted median MR allows for 50% of the instrumental variables to be invalid. MR-Egger regression allows all the instrumental variables to be subject to direct effects (i.e. horizontal pleiotropy), with the intercept representing bias in the causal estimate due to pleiotropy and the slope representing the causal estimate.
Smoking Status ~ LOAD
Figure 1 illustrates the SNP-specific associations with Smoking Status versus the association between each SNP and risk of LOAD.
Fig. 1: Scatterplot of SNP effects for the association of Trait and LOAD
Figure 2 and Table 1 shows the SNP-specific effects and overall IVW, weighted median and Egger regression causal estimates of genetically predicted Smoking Status on risk of LOAD.
Fig. 2: Forrest plot of Wald ratios and 95% CIs for SNP-specific and overall IVW, Weighted median and Egger associations
Figure 3 shows a funnel plot to detect pleiotropy and Table 2 show the results of Cochrans Q heterogeneity test to assess for the presence of pleiotropy.
Fig. 3: Funnel plot of the Trait – LOAD causal estimates against their precession
Smoking Status ~ AAOS
Figure 4 illustrates the SNP-specific associations with Smoking Status versus the association between each SNP and AAOS.
Fig. 4: Scatterplot of SNP effects for the association of Trait and AAOS
Figure 5 and Table 4 shows the SNP-specific effects and overall IVW, weighted median and Egger regression causal estimates of genetically predicted Smoking Status on AAOS.
Fig. 5: Forrest plot of Wald ratios and 95% CIs for SNP-specific and overall IVW, Weighted median and Egger associations
Figure 6 shows a funnel plot to detect pleiotropy and Table 5 show the results of Cochrans Q heterogeneity test to assess for the presence of pleiotropy.
Fig. 6: Funnel plot of the traitohol Conumption – AAOS causal estimates against their precession
Smoking Status ~ AB42
Figure 1 illustrates the SNP-specific associations with Smoking Status versus the association between each SNP and AB42.
Fig. 1: Scatterplot of SNP effects for the association of trait and AB42
Figure 2 and Table 7 shows the SNP-specific effects and overall IVW, weighted median and Egger regression causal estimates of genetically predicted Smoking Status CSF AB42 levels.
Fig. 2: Forrest plot of Wald ratios and 95% CIs for SNP-specific and overall IVW, Weighted median and Egger associations
Figure 3 shows a funnel plot to detect pleiotropy and Table 8 show the results of Cochrans Q heterogeneity test to assess for the presence of pleiotropy.
Fig. 3: Funnel plot of the trait – AB42 causal estimates against their precession
Smoking Status ~ Ptau
Figure 1 illustrates the SNP-specific associations with Smoking Status versus the association between each SNP and risk of Ptau.
Fig. 1: Scatterplot of SNP effects for the association of trait and Ptau
Figure 2 and Table 9 shows the SNP-specific effects and overall IVW, weighted median and Egger regression causal estimates of genetically predicted Smoking Status on risk of Ptau.
Fig. 2: Forrest plot of Wald ratios and 95% CIs for SNP-specific and overall IVW, Weighted median and Egger associations
Figure 3 shows a funnel plot to detect pleiotropy and Table 10 show the results of Cochrans Q heterogeneity test to assess for the presence of pleiotropy.
Fig. 3: Funnel plot of the trait – Ptau causal estimates against their precession
Smoking Status ~ Tau
Figure 1 illustrates the SNP-specific associations with Smoking Status versus the association between each SNP and CSF Tau levels.
Fig. 1: Scatterplot of SNP effects for the association of trait and Tau
Figure 2 and Table 12 shows the SNP-specific effects and overall IVW, weighted median and Egger regression causal estimates of genetically predicted Smoking Status on risk of Tau.
Fig. 2: Forrest plot of Wald ratios and 95% CIs for SNP-specific and overall IVW, Weighted median and Egger associations
Figure 3 shows a funnel plot to detect pleiotropy and Table 13 show the results of Cochrans Q heterogeneity test to assess for the presence of pleiotropy.
Fig. 3: Funnel plot of the trait – Tau causal estimates against their precession
Smoking Status ~ HV
Figure 1 illustrates the SNP-specific associations with Smoking Status versus the association between each SNP and hippocampal volume.
Fig. 1: Scatterplot of SNP effects for the association of trait and hippocampal volume
Figure 2 and Table 12 shows the SNP-specific effects and overall IVW, weighted median and Egger regression causal estimates of genetically predicted Smoking Status on risk of hippocampal volume.
Fig. 2: Forrest plot of Wald ratios and 95% CIs for SNP-specific and overall IVW, Weighted median and Egger associations
Figure 3 shows a funnel plot to detect pleiotropy and Table 13 show the results of Cochrans Q heterogeneity test to assess for the presence of pleiotropy.
Fig. 3: Funnel plot of the trait – hippocampal volume causal estimates against their precession